lstm binary prediction

lstm_binary_prediction

from random import randint
from numpy import array
from numpy import argmax
from pandas import concat
from pandas import DataFrame
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense

# generate a sequence of random numbers in [0, 99]
def generate_sequence(length=100):
    return [randint(0, 1) for _ in range(length)]

# one hot encode sequence
def one_hot_encode(sequence, n_unique=100):
    encoding = list()
    for value in sequence:
        vector = [0 for _ in range(n_unique)]
        vector[value] = 1
        encoding.append(vector)
    return array(encoding)

# decode a one hot encoded string
def one_hot_decode(encoded_seq):
    return [argmax(vector) for vector in encoded_seq]

# generate data for the lstm
def generate_data():
    # generate sequence
    sequence = generate_sequence()
    # one hot encode
    encoded = one_hot_encode(sequence)
    print(encoded)
    # create lag inputs
    df = DataFrame(encoded)
    df = concat([df.shift(4), df.shift(3), df.shift(2), df.shift(1), df], axis=1)
    # remove non-viable rows
    values = df.values
    values = values[5:,:]
    # convert to 3d for input
    X = values.reshape(len(values), 5, 100)
    # drop last value from y
    y = encoded[4:-1,:]
    return X, y

def predictions():
    # Generate sequence
    sequence = generate_sequence()
    # One hot encode
    encoded = one_hot_encode(sequence)
    # Create lag inputs
    df_encoded = pd.DataFrame(encoded)
    for i in range(4, -1, -1):
        df_encoded[f'Lag_{i}'] = df_encoded[0].shift(i)
    df_encoded.dropna(inplace=True)
    # Merge with 'Prediction' column
    df_encoded['Prediction'] = df['Prediction'].values[:len(df_encoded)]
    # Split into X and y
    X = df_encoded.drop(columns=['Prediction']).values
    y = df_encoded['Prediction'].values
    # Reshape X for LSTM input
    X = X.reshape((X.shape[0], 1, X.shape[1]))
    return X, y
# define model
model = Sequential()
model.add(LSTM(50, batch_input_shape=(5, 5, 100), stateful=True))
model.add(Dense(100, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])
# fit model
for i in range(2000):
    X, y = generate_data()
    model.fit(X, y, epochs=1, batch_size=5, verbose=2, shuffle=False)
    model.reset_states()
# evaluate model on new data
X, y = generate_data()
yhat = model.predict(X, batch_size=5)
print('Expected:  %s' % one_hot_decode(y))
print('Predicted: %s' % one_hot_decode(yhat))